Polyelectrolyte elastomer-based ionotronic sensors with multi-mode sensing capabilities via multi-material 3D printing

Stretchable ionotronics have drawn increasing attention during the past decade, enabling myriad applications in engineering and biomedicine. However, existing ionotronic sensors suffer from limited sensing capabilities due to simple device structures and poor stability due to the leakage of ingredients. In this study, we rationally design and fabricate a plethora of architected leakage-free ionotronic sensors with multi-mode sensing capabilities, using DLP-based 3D printing and a polyelectrolyte elastomer. We synthesize a photo-polymerizable ionic monomer for the polyelectrolyte elastomer, which is stretchable, transparent, ionically conductive, thermally stable, and leakage-resistant. The printed sensors possess robust interfaces and extraordinary long-term stability. The multi-material 3D printing allows high flexibility in structural design, enabling the sensing of tension, compression, shear, and torsion, with on-demand tailorable sensitivities through elaborate programming of device architectures. Furthermore, we fabricate integrated ionotronic sensors that can perceive different mechanical stimuli simultaneously without mutual signal interferences. We demonstrate a sensing kit consisting of four shear sensors and one compressive sensor, and connect it to a remote-control system that is programmed to wirelessly control the flight of a drone. Multi-material 3D printing of leakage-free polyelectrolyte elastomers paves new avenues for manufacturing stretchable ionotronics by resolving the deficiencies of stability and functionalities simultaneously.

1. The authors do not clearly discuss what we have learned from this work about general material design and how we can use their results to make structures with desired prescribed properties. Comments about these issues appear here and there in the paper, but I believe there should be a designated paragraph or two at the conclusion section addressing this extensively. Some related questions are, why did the authors choose the specific materials? how the printing parameters are chosen and optimised? How about the interface? How about the robustness of the printed structure in terms of the solvent resistance and thermal durability? …The chemical -physical assessments on the printed structure seem incomplete. Lost queries remains on the surface property, durability, etc.

Response:
We thank the reviewer for the constructive suggestions. The motivation of our work is that existing ionotronic sensors usually can only sense a single mode of deformation such as compression or tension, which significantly restricts the scope of applications. The limitation in sensing modes is due to the limitation in device structures, which in turn is due to the deficiency in manufacturing techniques. Moreover, the employed ionic conductors suffer from leakages and greatly hamper the stability of the ionotronic sensors. In our work, we resolve the deficiencies in sensing mode and stability simultaneously by synthesizing a new type of leakage-free polyelectrolyte elastomer and using DLP-based multi-material 3D printing technique to fabricate a variety of long-term stable iontronic sensors with multi-mode sensing capabilities. As shown in Figure R1 ( Figure 1 in the revised manuscript), we have fabricated various architected ionotronic sensors with multi-mode sensing capabilities, which mimic the multi-mode sensing performances of the human skin. We have demonstrated the fully multi-material 3D printed sensors capable of sensing tension, compression, shear, and torsion, as well as combined tension and compression, combined compression and shear, and combined torsion and compression without signal cross-talks. This is the first work that reports an approach to design and manufacture fully 3D printed ionotronic sensors that are capable of sensing multiple modes of deformation. Figure R1 (Figure 1 in the revised manuscript). Skin-mimicked ionotronic sensors with multi-mode sensing capabilities by DLP-based multi-material 3D printing. (a) Schematic of the human skin containing various mechanoreceptors. The SAI responds to touch and static pressure, the SAII responds to stretching, the RAI responds to touch and dynamic pressure, and the RAII responds to deep pressure and vibration. (b) Human skin is capable of multi-mode sensing, such as compression, tension, combined compression and shear, and combined torsion and compression, without signal interferences. (c) 3D printing of various architected ionotronic sensors for multimode sensing using polyelectrolyte elastomers with robust interfaces and without leakage.
For the general material design, we have rewritten the sentences about polyelectrolyte elastomers in the Introduction: "Polyelectrolyte elastomers with either cations or anions fixed to the backbones provide a promising remedy for endurant ionotronics by simultaneously resolving the predicaments of solvent-leakage and ion-leakage." Furthermore, in the characterizations of PEE, we have added the synthetic strategy, the schematic of the synthetic process, and the chemical structures of ingredients used for PEE in the revised Figure 2, as shown in Figure R2. The key idea is to use an ionic monomer containing an acrylate functional group for free radical photo-polymerization such that, one type of ions will be engrafted to the polymer network and resistant to leakage and the other type of ions will be confined within the polymer network as well due to electrostatic interactions. In addition, we have explained the mechanism of being resistant to ion leakage of PEE in more detail in the descriptions of the revised Figure 3h: "When in contact with DE, the ions in the PEE tend to diffuse toward the DE due to the concentration gradient. However, the directional diffusion of the fixed anions exerts tensile stress on the polymer chains of PEE, which counteracts the chemical potential of the anions to prevent long-range diffusion (Figure 3h). Meanwhile, the directional diffusion of the mobile cations is also prohibited due to the electrostatic interactions.

Consequently, PEE is resistant to ion leakage and the PEE-based ionotronic sensor is stable.".
We attach the schematic in Figure R3. Now we believe the general material design should be clear.

Figure R3 (Figure 3h in the revised manuscript). Schematics illustrating the prohibition of ion leakage in the PEE-based sensor.
As for how to make the structures with desired prescribed properties, the multi-material 3D printing capability allows one to rationally design the structures of the sensor with high flexibility. For example, since the capacitance change of the compressive sensor is inversely proportional to the thickness of DE layer, and the sensitivity is inversely proportional to the stiffness of DE layer, we can increase the sensitivity of the sensor by introducing microstructures to the DE layer, as shown in Figure R4. As another example, since the capacitance change of the shear sensor for a differential increment in shear displacement dl is proportional to the effective overlap area of the two PEE layers, given by where w is the characteristic dimension in the width direction, we can tailor the sensitivity by programming the pattern of the front line. As shown in Figure R5  As the third example, since the capacitance change of the torsional sensor for a differential increment in twist angle is proportional to the effective overlap area of the two PEE layers, given by where n is the number of division and r is the radius of the sensor, we can tailor the sensitivity by programming the pattern of the PEEs. As shown in Figure R6, we have designed two patterns and obtained two sensitivities. Moreover, as shown in Figure R7, we have designed and fabricated three types of integrated ionotronic sensors that can sense combined deformations without mutual signal interference. For each design, we have explained the designing principles and the details can be found in the corresponding texts in the revised manuscript. To sum up, both the general material design and the structure-property relationships have been explained in detail in the revised manuscript. The key idea is to use an ionic monomer containing an acrylate functional group for free radical photo-polymerization such that, one type of ions will be engrafted to the polymer network and resistant to leakage, and another type of ions will be confined within the polymer network as well due to electrostatic interactions. Although the principle is generic, one has to choose specific chemistries for deployments. In our work, following the general principle of polyelectrolyte elastomers, we synthesize a photocurable ionic monomer BS ( Figure R8a-b) and then synthesize the PEE through photo-polymerization ( Figure R8c). We have added detailed information about the material system used in our work in the revised manuscript, including the synthetic strategy, the schematic of the synthetic process, and the chemical structures of the ingredients used for PEE, as shown in Figure R8. For the printing parameters and optimization, we have conducted more studies regarding the in-situ photo-rheological behaviors and the energy density-layer thickness relationships (as shown in Figure R9), and added a representative printed multi-material grid structure to the revised manuscript. We have added the following sentences to Page 9 in the revised manuscript: "We perform in-situ photo-rheological characterizations to investigate the photoreactivity of PEE and DE. As shown in Figure 3a, (Figure 3c).". For the interface, we have characterized the interfacial toughness by measuring the adhesion energy, as shown in Figure R10. For more clarity, we add more details about the cohesive rupture of the printed sample and the adhesive rupture of the assembled sample to the revised Figure 3: "We perform the 180° peeling tests to assess the interfacial toughness of 3D printed and manually assembled   For thermal durability, we have performed TGA measurements. TGA reveals that the PEE is thermally stable up to 275 °C, as shown in Figure R11, which should be sufficient for most engineering applications. For testing solvent resistance, we soaked samples in water for 7 days. After taking out and exsiccating the samples, we measured the changes in mass and conductivity. For comparison, we also performed control experiments using a LiTFSI doped elastomer (abbreviated as LiE). The PEE maintains weight and conductivity well whereas the LiE dramatically loses weight and conductivity, by 26.6% and 98.4%, respectively, as shown in Figure R12. We also tested the solvent stability in organic solvent. As shown in Figure R13, soaking in methyl cyanide (MeCN) leads to similar results that the weight and conductivity change negligibly for PEE but enormously for LiE. Besides, the PEE maintains shape well while the LiE becomes ragged after the test. These results have been added to the supplementary materials. Furthermore, we have carried out more assessments for the material and the printed sensors, including the FTIR spectrum and transmittance measurements, cytotoxicity tests, and response time of the sensing elements of the control unit, as shown in Figure   R14. The details can be found in the revised manuscript and revised supplementary materials.  For more clarity, we have added a paragraph to describe the details of peeling test in the Experimental Section in the revised manuscript: "180° peel test was performed to measure the adhesion energy between PEE and DE, using the Instron 5966 with a 100N load cell at a constant peeling speed of 30 mm min −1 . A stiff backing was bonded to each layer of samples using double-sided mesh tape. For the printed sample, the sample was printed with dimensions of 60 mm × 10 mm × 1 mm (0.5 mm for each layer) and a 10 mm long pre-crack was made. For the assembled sample, the sample was prepared by assembling a piece of PEE (60 mm × 10 mm × 0.5 mm) and a piece of DE (60 mm × 10 mm × 0.5 mm) and then the sample was stood for 30 minutes before the test." Comment 1.2.3. In Fig.2, the structure -capacitive property relationship has not been clearly defined with the structural variables. In Fig 2d, the pressure seems large, not sure this will bring any advantage in the applications.

Response:
We have revised the structure -capacitive property relationship both in the revised Figure 4 and in our response to Comment 1.1. In particular, whereas the key innovation of our work is the multi-mode sensing capabilities of the printed ionotronic sensors, we have introduced microstructures to the DE layer of the compressive sensor, improved its sensitivity by two orders of magnitude, and reduced the magnitude of pressure to the order of 10 kPa, as shown in Figure R17.

Response:
We thank the reviewer for the constructive suggestions. We have added the original Figure S13 to the manuscript in the revised Figure 6. Furthermore, we have characterized the response times of the shear sensor and the compressive sensor of the control unit, and added them to the revised supplementary materials in Figure S22, as shown in Figure R18. The response times are measured to be 52 ms for the shear sensor and the compressive sensor, respectively, within the capabilities of our measuring instruments.

Comment 1.4:
Another major flaw is that the manuscript is full of typos, mistakes in both grammar and syntax, and in many cases incomprehensible. I will not enumerate all the issues here, but I advise the authors to carefully edit their paper before resubmitting to avoid sloppiness.

Response:
We thank the reviewer for the kind suggestions. We have carefully and thoroughly polished the text.

Comment 1.5:
In conclusion, the quality of manuscript doesn't meet the merits for publishing in Nat Comm. It might fit more to some specified journal i.e. Sci. rep. etc. I would recommend to reject or transfer.

Response:
We thank the reviewer again for the constructive comments and suggestions.
By following them, we have thoroughly revised our manuscript and significantly improve the quality of our work. Therefore, we sincerely ask the reviewer to re-evaluate the possibility of our work to be published in Nature Communications.

Reviewer #2:
General Comment: In this manuscript, Li and coauthors reported ionotronic sensors fabricated by multi-materials 3D printing technologies. They show different type of sensors such as tensile, pressure, shear, and torional sensors. They also show the demonstrate a multi-functional ionotronic sensor that can decipher the signals of compression, tension, or their combination. Finally they demonstrate a wearable remote-control unit that can wirelessly communicate with a drone. The claim of this manuscript is that the multi-material 3D printing allows high flexibility in the structural design of the sensors, enabling the sensing of multiple modes of mechanical stimuli, and that the polyelectrolyte elastomer is stretchable, conductive, and resistant to ion leakage, contributing to the extraordinary durability of the sensors. The results are well organized and enough good to be published; however, the reviewer requests major revision.

Response:
We appreciate the reviewer for the comprehensive summary and positive remarks on our work. In the following, we have addressed the reviewer's comments point-by-point. Whereas we would like to keep the Introduction section more focused, for more comparison, we have added the following sentences to the first paragraph of the Results and Discussions section in the revised manuscript: "In general, ionotronics sensors are softer and more stretchable than their electronic counterparts. In addition, since ionotronic sensors also employ ions as the charge carrier, they potentially provide a more seamless interface with the biological systems." Comment 2.3: As shown in Figure 3, separation of multiple strain information is important. I'm curious to know whether shear and torsional sensors can separate signals of such target strains from pressure and tensile strains?

Response:
We thank the reviewer for the constructive suggestions. Yes, 3D printing allows the design and fabrication of integrated ionotronic sensors that can separate shear and torsion from compression. In addition to the integrated tensile and compressive sensor, we have added two more examples to demonstrate these performances to the revised Figure 5, as shown in Figure R19. One is an integrated compressive and shear sensor that can sense compression, shear, or their combination without signal cross-talk. Another is an integrated torsional and compressive sensor that can sense compression, torsion, or their combination without signal cross-talks. Comment 2.4: Page 10 Line 224, "which has not been realized before."; Since there are shear/ torsional sensors, the reviewer guess that the authors try to mean that "which has not been realized before using ionotronic sensors". This sentence should be revised to clarify the meaning.

Response:
We thank the reviewer for the rigorous reading. Yes, we mean that "which has not been realized before using ionotronic sensors". We have rewritten the first sentence in the last paragraph on Page 13 as follows:  As for the corresponding texts, we have rewritten the sentences to include more details.
For the shear sensor: elusive" mean. This sentence is little bit too long to understand appropriately, so I suggest to dividing into some sentences.

Response:
We have rewritten the sentence to be more concise as follows: "The ionically conductive elastomers synthesized by dissolving lithium salt (e.g. lithium bis(trifluoromethanesulfonyl)imide (LiTFSI)) or zwitterions into elastomer matrices are immune to solvent leakage, but they are still susceptible to ion leakage. The persistent concentration gradient between the interior and the exterior keeps driving the mobile ions to diffuse outwards when in contact with other elastomers." B) Page 3, Line 53, "the stability of stretchable ionic conductors aside ,": why do authors aside the stability? Normally the stability is an important issue and thereby should not be ignored. If the authors mean that the stability issue is not the focus of this manuscript, I don't think this phrase is needed here.

Response:
We have rewritten the sentence as follows: "Nevertheless, in addition to the stability of the materials, manufacturing of the devices has been another long-standing hurdle for the development of the field.".
C) Page 4, Line 76, "despite the elaborate structures": I don't undertand the meaning of this. The authors describe "simply print one single material" in the previous sentence, which does not correspond to the following sentence.

Response:
We have rewritten the sentence as follows: "Moreover, the perceivable stimuli of these printed ionotronic sensors are mostly limited to compression and/or tension (Table S1)." D) Page 4, Line 79, "whereby" is not appropriate here.

Response:
We have rewritten the sentence as follows: " An additional sample demonstration should be added to clarify the diverse functionalities. So, this reviewer thinks this work needs to be improved to display its significance before acceptance.

Response:
We thank the reviewer for the kind suggestions to help us improve our work for acceptance. Our work does not simply integrate some reported works. Existing ionotronic sensors usually can only sense a single mode of deformation such as compression or tension, which significantly restricts the scope of applications. The limitation in sensing modes is due to the limitation in device structures, which in turn is ascribed to the deficiency in manufacturing techniques. Moreover, the employed ionic conductors suffer from leakages and greatly hamper the stability of the ionotronic sensors. In our work, we resolve the deficiencies in sensing mode and stability simultaneously by synthesizing a new type of leakage-free polyelectrolyte elastomer and using the DLP-based multi-material 3D printing technique to fabricate a variety of long-term stable ionotronic sensors with multi-mode sensing capabilities. As shown in Figure R21 ( Figure 1 in the revised manuscript), we have fabricated various architected ionotronic sensors with multi-mode sensing capabilities, which mimics the multi-mode sensing performances of the human skin. We have demonstrated the sensing of tension, compression, shear, and torsion, as well as combined tension and compression, combined compression and shear, and combined compression and torsion without signal cross-talks. This is the first work that reports an approach to design and manufacture fully 3D printed ionotronic sensors that are capable of sensing multiple modes of deformations. The SAI responds to touch and static pressure, the SAII responds to stretching, the RAI responds to touch and dynamic pressure, and the RAII responds to deep pressure and vibration. (b) Human skin is capable of multi-mode sensing, such as compression, tension, combined compression and shear, and combined torsion and compression. (c) 3D printing of various architected ionotronic sensors for multi-mode sensing using polyelectrolyte elastomers with a robust interface and without leakage.
To achieve the above-mentioned goal, on one hand, we have designed and synthesized a new type of polyelectrolyte elastomer that is stretchable, transparent, ionically conductive, thermally stable, and leakage-resistant. In particular, to achieve the merits of the polyelectrolyte elastomer, we have synthesized the photopolymerizable ionic monomer, BS, by ourselves. We might not describe the material clearly enough in the original manuscript. In the revised manuscript, we perform more comprehensive characterizations for the material and devote Figure 2 for material characterizations, as shown in Figure R22. On the other hand, DLP-based multi-material 3D printing is a well-established technique and anyone can use it to fabricate their own structures. Nevertheless, the printing protocols often need to be modified whenever a new material has been used.
In our work, we modify the printing parameters to comprise the printing of the new type of polyelectrolyte elastomer such that, the printed samples do not have muchdeteriorated properties and the printed ionotronic sensors have a robust interface and decent resolution. We add Figure 3a-3c, regarding the in-situ photo-rheological properties, energy density-layer thickness relationships, and a representative printed grid structure, in the revised manuscript, as shown in Figure   We agree with the reviewer that the exact strain and sensitivity should be clarified for specific applications. However, we would like to emphasize that the key point of our work is to realize long-term stable ionotronic sensors with multi-mode sensing capability. Poor stability and the lack of functionalities are two central challenges in existing ionotronic devices. Our work resolves these two issues simultaneously, by synthesizing a new type of leakage-free polyelectrolyte elastomer and using the DLPbased multi-material 3D printing technique for fabrication, as discussed above in Figure   R21.
By taking advantages of multi-material 3D printing, we have shown that the sensing of shear and torsion can also be realized through rational structure design. Furthermore, although pushing the limits of the sensing performances of ionotronic sensors is not the focus of our work, we would like to emphasize that, for the sake of multi-material 3D    Further comments are noted below: Comment 3.1: In the introduction part, the motivation of this work is not clearly explained. The manuscript should point out the exact strain, sensitivity needed in stretchable ionotronics for sensing.

Response:
The motivation of our work is that existing ionotronic sensors suffer from poor stability and lack of functionalities, due to the poor stability of the employed ionic conductors and the simple device structures limited by manufacturing technique, which greatly impedes their practical applications. Our work resolves these two issues simultaneously by synthesizing a new type of leakage-free polyelectrolyte elastomer and using the DLP-based multi-material 3D printing technique to fabricate a variety of long-term stable iontronic sensors with multi-mode sensing capabilities.
As for the exact strain and sensitivity, again, we agree with the reviewer that the exact strain and sensitivity should be clarified for specific applications. However, the key point of our work is to realize long-term stable ionotronic sensors with multi-mode sensing capability. Poor stability and the lack of functionalities are two central challenges in existing ionotronic devices. Our work resolves these two issues simultaneously, by synthesizing a new type of leakage-free polyelectrolyte elastomer and using the DLP-based multi-material 3D printing technique for fabrication. We have modified Figure 1 to highlight our key point in the revised manuscript. As for the structures and the sizes of sensors, detailed information can be found in Figure 4 and Figure 5, as well as Figures S14-S20. For example, we have provided the details about torsional sensors in Figure S18, as shown in Figure R28.

Response:
We have carefully and thoroughly double-checked the text.

Comment 3.5:
For the claimed application as controller system attached on human skin, more bionic experiment and cell cytotoxicity test should be provided.

Response:
We thank the reviewer for the constructive suggestion. Since the printed controller consists of PEE and DE, we have performed cytotoxicity tests for the two constituent materials. Both materials exhibit low cell cytotoxicity, as shown in Figure   R30. We add the results in Figure S8.

REVIEWER COMMENTS
Reviewer #1 (Remarks to the Author): The authors have tried to address my previous concerns; however, considerable gap remains from the scientific perspective. The author performed more characterizations. The key is to bring a clear scientific interpretation, rather to pile up the data.
Regarding to my first query, a mechanics study is mandatory to explain the sensing function of a single mode of deformation such as compression or tension. The current interpretation is far from enough. A FEA analysis can do the job, I suppose. I have second queries on the linearity of plots in Fig. R5 and Fig.  R6. The trends appear to me that they are more link segment of a nonlinear curve, the current fit seems very rough.
For the photo-curing assessment, there is a concern on the gradient caused by the curing process, it will form 'stiff skin' on the top, which is a concern to create bilayer structure, did author note this phenomenon?
The responses to my comments 1.2 and 1.3 didn't really hit the point, I would recommend author to check and explain a clear answer. In conclusion, the revision has brought some improvements to the manuscript. However, more clarifications will be needed to make the manuscript reach the threshold for publishing in Nat. Comm. I would recommend a major revision.

Reviewer #2 (Remarks to the Author):
I'm pleased to see that authors addressed all the comments appropriately. My remaining minor comment is that Figure 4 caption should have details of cyclic conditions such as "a cyclic shear test with a maximum shear strain of 66.7% (Figure 4h)" and "cyclic torsional test with a maximum twist angle of 30° (Figure 4i)".

Reviewer #3 (Remarks to the Author):
The study focuses on addressing the limitations of existing ionotronic sensors by developing a new fabrication approach and material design. This approach allowed them to create sensors capable of sensing tension, compression, shear, and torsion, with customizable sensitivities achieved by programming the device architectures. The use of leakage-free polyelectrolyte elastomers in multimaterial 3D printing opens new possibilities for manufacturing stretchable ionotronics. By addressing the limitations of stability and functionality simultaneously, this study represents an important advancement in the field of ionotronic sensor technology.
1.The intrinsic properties of the elastomers, including mechanical strength and electrical conductivity, do not demonstrate notable advantages. Similar studies addressing these properties have also been observed frequently in the literature. This reviewer firmly believes that the significance of developing diverse novel applications lies in the utilization of base materials with exceptional performance.
2.The relevant applications of a wearable remote-control unit should be further described in detail, including the design of printed circuit boards and the analysis of multi-channel data acquisition.
3.In the video demonstration, the sensors are adhered to the surface of a rubber glove. Further characterization of the adhesive strength of the devices on surfaces such as skin and fabric can be conducted to demonstrate their potential as wearable devices. Additionally, considerations such as skinfriendliness, breathability, and flexibility of the adhesive materials should be taken into account to ensure user comfort and long-term wearability.

4.Similarly, the deformation of ion-conductive materials can result in changes in electrical resistance.
The question arises whether these resistance variations can impact the capacitance response characteristics of the devices. In general, when the resistance value undergoes variations, the capacitance value can also change accordingly.
5.When collecting data from sensors with multiple channels, the possibility of signal crosstalk does exist.
Signal crosstalk refers to the interference or coupling of signals between different channels, which can affect the accuracy of data acquisition. How to avoid signal crosstalk in separation of multiple strain information?

Response: The authors would like to thank the editors and reviewers for their
valuable comments which help us further improve the quality of our paper. We have carefully addressed the reviewers' comments and suggestions point-by-point and revised our paper accordingly. Our responses to each of the comments are as follows.
Reviewer #1: The current interpretation is far from enough. A FEA analysis can do the job, I suppose.
I have second queries on the linearity of plots in Fig. R5 and Fig. R6. The trends appear to me that they are more link segment of a nonlinear curve, the current fit seems very rough.

Response:
We thank the reviewer for the constructive suggestions. We have performed FEA for a more in-depth understanding of the sensing functions of the tensile sensor using the COMSOL Multiphysics.
As shown in Figure R1, we model the tensile sensor using the explicit geometries.
Because the two ends of the dumbbell-shaped sample have larger cross-sectional areas and are glued to two acrylate plates for clamping, the majority of tensile deformation occurs in the central segment under uniaxial tension. During deformation, the area increases and the thickness shrinks such that the capacitance increases. Assume the materials to be incompressible, the original length, width, and thickness of the central segment to be l0, w0, and t0. When the length is strained to (1 + ) 0 (ε is the tensile strain), due to the Poisson's effect, the width and the thickness become 0 √1 + ⁄ and 0 √1 + ⁄ , respectively. Consequently, ∝ (1+ ) 0 × 0 √1+ ⁄ 0 √1+ ⁄ ∝ and ΔC/C0 is linearly proportional to the tensile strain. The FEA result shows that ΔC/C0 varies with tensile strain mostly in a linear manner, which is in satisfactory agreement with the experiment, as shown in Figure R1b. The small deviation should be due to the discrepancies in the specific deformation, boundary conditions, and material properties, such as the compressibility of the materials, between FEA and the experiment. In addition, the distributions of strain, capacitance, and electric field before and after deformation have been shown.
We have added Figure R1 as Figure  Furthermore, we have also performed FEA for a more in-depth understanding of the sensing functions of the integrated tensile and compressive sensor. Recall that we have rationally designed the sensor structure such that the two independent electrodes of the sensor are separated much farther than the thickness of the DE layer to minimize signal cross-talks. The DE layer of the tensile sensor is much thinner than the DE layer of the compressive sensor so the relative thickness change of the DE layer of the compressive sensor is negligible when the tensile sensor is activated. As shown in Figure R2, FEA results confirm that the compressive unit deforms substantially whereas the tensile unit deforms negligibly when the sensor is under compression. Specifically, for a normal compressive strain of 25%, the principal strain of the compressive unit is ~0.328 and the principal strain of the tensile unit is only ~0.00873. Figure R2b shows the variation of ΔC/C0 with compressive strain for capacitance meters C1 and C2. The capacitance of C1 increases while the capacitance of C2 barely changes with compressive strain, which is consistent with experimental results. The distributions of principal strain, potential, and electric field, as well as the capacitances of C1 and C2 before and after deformation have been shown in Figure R2c-2l. We have added Figure R2 as Figure   Specifically, for a normal tensile strain of 100%, the principal strain of the tensile unit is ~0.483 and the principal strain of the compressive unit is only ~0.0029. Figure R3b shows the variation of ΔC/C0 with tensile strain for capacitance meters C1 and C2. The capacitance of C2 decreases while the capacitance of C1 barely changes with tensile strain, which is consistent with experimental results. The distributions of principal strain, potential, and electric field, as well as the capacitances of C1 and C2 before and after deformation have been shown in Figure R3c-3l. We have added Figure R3 as Figure   (b) ΔC/C0 varies with tensile strain for capacitance meters C1 and C2. Principal strain field distributions at undeformed state (c) and at a tensile strain of 100% (d). Potential field distributions at undeformed state (e) and at a tensile strain of 100% (f). Electric field distributions at undeformed state (g) and at a tensile strain of 100% (h). The capacitances of C1 at undeformed state (i) and at a tensile strain of 100% (j). The capacitances of C2 at undeformed state (k) and at a tensile strain of 100% (l).
Moreover, we purposely simulate the responses of a badly designed sensor, i.e. the two independent electrodes of the sensor are relatively close to each other and the DE layer of the tensile sensor is even thicker than the DE layer of the compressive sensor, for comparison. The dimensions of the badly designed sensor are shown in Figure R4a.
FEA results confirm that the global tensile deformation of the sensor not only causes the tensile unit to deform substantially but also causes the compressive unit to deform somewhat, especially for the adjacent regions. Specifically, for a normal tensile strain of 50%, the principal strain of the tensile unit is ~0.384 and the principal strain of the compressive unit reaches ~0.0375. Figure R4b shows that both the capacitances of C1 and C2 change notably with tensile strain. The bending deformation at the central region is due to the asymmetric stress distribution that the stress at the bottom is larger than the stress at the top, resulting in a bending moment. The distributions of principal strain, potential, and electric field, as well as the capacitances of C1 and C2 before and after deformation have been shown in Figure R4c-4l. We have added Figure R4 as Figure   S24 in the revised Supplementary Materials. and at a tensile strain of 50% (d). Potential field distributions at undeformed state (e) and at a tensile strain of 60% (f). Electric field distributions at undeformed state (g) and at a tensile strain of 50% (h). The capacitances of C1 at undeformed state (i) and at a tensile strain of 50% (j). The capacitances of C2 at undeformed state (k) and at a tensile strain of 50% (l).
We have added the following sentences on page 18 in the revised manuscript: "FEA results also show prominent differences between the signals of C1 and C2 when the sensor is subject to compression ( Figure S22) or tension ( Figure S23). The key point of signal decoupling is to minimize the associated deformation of one sensor when another sensor is deformed through appropriate structure design. As a counterexample, both the capacitances of C1 and C2 of a badly designed sensor change notably with tensile strain ( Figure S24)." In addition, we have added a section entitled "Finite element analysis" to the Method section in the revised manuscript: "FEA was performed using the commercial package COMSOL Multiphysics 6.0.
According to the experimental data, both the PEE and DE were modeled as incompressible neo-Hookean materials with shear moduli of 90.7 kPa and 294 kPa, respectively. DE was modeled as a linear dielectric material with relative dielectric constant εr = 3, and PEE was simplified as an equipotential body. The deformation caused by electrostatic force was ignored.
Tensile sensor. The upper PEE was applied with a voltage of 1 V and the lower PEE was grounded. For boundary conditions, the left side of the sensor was fixed and the right side was subjected to a specified displacement L. The maximum value of the specified displacement L was set to be 10 mm. and L2 is 2.5 mm for the badly designed sensor." As for the linearity of the plots in Fig. R5 and Fig. R6, i.e. the ΔC/C0 versus shear strain curves and the ΔC/C0 versus twist angle curves, we would like to clarify that the three ΔC/C0 versus shear strain curves are not all linear. For a flat front-line design, the curve is linear because ∆ ∝ ∆ (red curve). For a zigzag pattern design, the curve is quadratic because ∆ ∝ (∆ ) 2 (green curve). For a parabolic pattern design, the curve is cubic because ∆ ∝ (∆ ) 3 (blue curve). The R 2 values of the three fittings are 0.99916, 0.99998, and 0.99998, respectively. For the ΔC/C0 versus twist angle curves, the fittings indeed deviate from the experimental data, especially at small twist angles.
The reasons are as follows. First, the difference between the bisection and the quartering designs is theoretically small at small twist angles. Second, the absolute capacitances of the two types of sensors are small, on the order of 1 pF, due to the large distance between the two electrodes and the presence of air, so the signal-to-noise ratio is low and the measured capacitance of the sensor can be profoundly affected by noise. Third, we have used a self-built LabVIEW-controlled torsional loading system to apply torsion to the sensor and the lack of controlling accuracy and stability might cause additional noise to the measured capacitance. Nevertheless, the linear fittings are satisfactory when the twist angle is larger than ~10°. The R 2 values of the two fittings are 0.99641 and 0.99273, respectively. Better fittings could be achieved through further optimizations by, e.g. reducing the distance between the two electrodes and using a more reliable loading systems, but these are out of the scope of current work.

Comment 1.2:
For the photo-curing assessment, there is a concern on the gradient caused by the curing process, it will form 'stiff skin' on the top, which is a concern to create bilayer structure, did author note this phenomenon?

Response:
We appreciate the reviewer for this insight. For 3D-printed thin laminated structures, the stiffness gradient might cause the bending of the structures due to residual stress or inhomogeneous swelling. However, each printed layer only has a thickness of ~50 μm while the overall thickness of the printed sensors is on the order of 1 mm in our experiments. Note that the bending stiffness is proportional to the thickness to the third power, ∝ 3 , where E is Young's modulus, I is the moment of inertial of the cross-section, and H is thickness. As a result, the stiffness gradient barely affects the properties of our printed sensors. Furthermore, we purposely placed the printed structure in a UV oven for 1 hour for complete curing of the printed elastomers to alleviate the influences of gradient curing. Both the thicknesses of each printed layer and the sensor and the post-curing treatment have been stated in the Method section of the manuscript: "…and the layer thickness was 50 μm.", "After printing, we used ethanol to rinse the printed structure and remove the uncured precursor. Subsequently, we placed the printed structure in a UV oven with 365 nm wavelength for 1 hour for complete curing of the elastomers."

Response:
We thank the reviewer for the reminder. We have carefully re-checked the previous comments 1.2 and 1.3.
We have addressed previous comments 1.2.1 and 1.2.3 in the last response. As for previous comment 1.2.2, "In Fig1f, the testing method needs a diagram to illustrate the process with defined parameters;", we have added Figure 3e and Figure 3f in the last response to compare different failure modes of printed sample and assembled sample but without giving a diagram to illustrate the 180° peeling test with defined parameters.
As shown in Figure R5, we now add a schematic with defined parameters to illustrate the 180° peeling test and add it to the revised Supplementary Materials as Figure S12.
The corresponding sentence on page 12 has been rewritten as: "The adhesion energies, given by the plateaued normalized force, 2Fss/W, where Fss is the steady-state peel force and W is the width of the sample ( Figure S12)  Interface can be demonstrated, the current demonstration seems not the best to show the advantage for this work." The key point of our work is to resolve the deficiencies in sensing mode and sensing stability in ionotronic sensing simultaneously, by synthesizing a new type of photo-curable leakage-free polyelectrolyte elastomer and using the DLP-based multi-material 3D printing technique to fabricate a variety of longterm stable ionotronic sensors with multi-mode sensing capabilities. Sensors that can sense mechanical stimuli are important for, e.g. biological systems to perceive and interact with the surroundings for adaption and survival and robots for accurate manipulation and safe human-machine interactions. We have demonstrated diverse ionotronic sensors capable of sensing various mechanical stimuli, including tension, compression, shear, and torsion, as well as combined tension and compression, combined compression and shear, and combined compression and torsion without signal cross-talks. In our work, we focus on the materials, mechanics, manufacturing, and performances of the sensors within the paradigm of ionotronics, and use commercial products for other electronic units.
We totally agree with the reviewer that a lot of capacitive-type human-machine interfaces can be demonstrated. Recall that one of the advantages of our work is the ability to fabricate a variety of ionotronic sensors with multi-mode sensing capabilities with the printable leakage-free polyelectrolyte elastomer and the DLP-based multimaterial 3D printing technique. In this sense, our demonstration shows the advantages well with the following reasons. First, the remote-control sensor of the demonstration is an integrated sensor that can sense compression and shear. Second, the spatial layout of the four shear sensors and the compressive sensor are rationalized such that the five sensing channels can be well decoupled with mitigated signal cross-talks. Third, the sensing mechanism of the shear sensor is different from the previous one (Figure 4e) and is purposely designed to behave like a switch. As shown in Figure R7, the sensor accommodates a capacitor due to the air in series with two capacitors due to the electric double layer. Upon shear, the PEEs come into contact with each other to eliminate the air capacitor, resulting in a giant capacitance change by orders of magnitude.
Experimental results show that the capacitance changes by more than 10 4 times upon stimulation. Such a giant signal is noise-tolerant and highly beneficial for circuit design.
Note that the feasibility of achieving the above-mentioned functionalities of the remotecontrol sensor is attributed to the high flexibility in the structure design enabled by the DLP-based multi-material 3D printing technique. Therefore, whereas many capacitivetype human-machine interfaces can be demonstrated, the printed wearable wireless remote-control unit for a drone shows the advantage of our work well. This reviewer firmly believes that the significance of developing diverse novel applications lies in the utilization of base materials with exceptional performance.

Response:
We would like to emphasize that the key point of our work is to resolve the deficiencies in sensing mode and stability, two pervasive but vital limitations for ionotronic sensing. We do so by synthesizing a new type of leakage-free polyelectrolyte elastomer and using the DLP-based multi-material 3D printing technique to fabricate a variety of long-term stable iontronic sensors with multi-mode sensing capabilities.
For the materials, we totally agree with the reviewer that significant development of applications relies on the utilization of base materials with exceptional performances.
On one hand, the polyelectrolyte elastomer has to be solvent-free to avoid solvent leakage. On the other hand, at least one type of ion should be fixed to the polymer network to avoid ion leakage. However, fixing ions to the polymer network inevitably restricts the mobility of ions and thus reduces the ionic conductivity. As a result, polyelectrolyte elastomers intrinsically possess relatively low ionic conductivity (typically 10 -5 -10 -3 S m -1 ), lower than that of gel-based ionic conductors such as ionic hydrogels and ionogels (10 -2 -10 1 S m -1 ) by orders of magnitude. Our newly designed and optimized polyelectrolyte elastomer, p(BS-co-MEA), exhibits balanced mechanical and electrical properties, enabling a variety of long-term stable iontronic sensors with multi-mode sensing capabilities. Compared to other polyelectrolyte elastomers, e.g. the recent ones reported by Kim et al. in 2020 in Science (Ref. 17 in the manuscript), our p(BS-co-MEA) shows comparable mechanical and electrical properties. We compare the conductivity, elongation at break, and Young's modulus of the two materials in Table   R1. We agree that designing and synthesizing polyelectrolyte elastomers of exceptional performances will be beneficial for high-performance ionotronic sensors, which requires additional studies but is beyond the scope of current work.

Comment 3.2:
The relevant applications of a wearable remote-control unit should be further described in detail, including the design of printed circuit boards and the analysis of multi-channel data acquisition.

Response:
We thank the reviewer for the constructive suggestions.
First, for more clarity, we add a digital image and two schematics to show the detailed geometrical information of the remote-control sensor, as shown in Figure R8. We have added the figure to the revised Supplementary Materials as Figure S26. The corresponding sentence on page 21 has been rewritten as: "The remote-control unit integrates five sensors: one compressive sensor and four shear sensors ( Figure S26), which are used as the input ports." Schematic of the cutaway view with relevant dimensions indicated. The unit is mm.
Second, we have rewritten the "Remote-control system for a drone" section in the Method section to add more detailed descriptions about the design of the printed circuit boards and the analysis of multi-channel data acquisition in the revised manuscript as follows: "A remote-control unit consisting of one compressive sensor and four shear sensors was designed and 3D printed. A copper wire was inserted into the cylindrical PEE(C5) during the printing process. Four copper wires were fixed to each side of the remote-control unit with PEE precursor. The five sensors of the remote-control unit shared the same ground electrode and were separately connected to five channels of a multiple relay, which was then connected to an LCR meter (TH2838A, Changzhou Tonghui Electronic Co. Ltd, China). The relay received and processed one signal once at a time and its on-off state was regulated by the digital signals sent from an Arduino UNO board. The output end of the Arduino UNO board was connected to five channels of another multiple relay, which was further connected to the corresponding pins of the PCB of the drone controller by welding. The LCR meter operated at medium speed with a frequency of 1 kHz and a voltage of 0.5 V. The real-time capacitances and the normalized capacitances of the five sensors were measured in a loop one after another by an LCR meter and were recorded by a LabVIEW program (National Instruments, Austin, TX, USA), and were displayed on the computer screen. After the program got started, the capacitance of each sensor was measured 20 times and then the average value was taken as the initial capacitance. Subsequently, the capacitance increased upon the loading of a finger. Once the normalized capacitance C/C0 of a sensor was larger than the prescribed threshold, the corresponding pin was switched on and a corresponding signal was generated, which eventually lead to an operation command for the drone. During operation, the remote-control unit was worn on the hand back and connected to a controlling circuit containing two relays, an LCR meter, a LabVIEW controlling program, and an Arduino board ( Figure S30). In response to the perturbation of a finger, the capacitance of a sensor increased. When the value of C/C0 exceeded a threshold, the circuit was switched on and a corresponding steering order was sent to the drone. The thresholds for capacitors C1, C2, C3, and C4 were set to be 1000 and the threshold for C5 was set to be 1.1. The order of "flip" was a pre-order that the signal of C5 should be followed by another order of one of the other four sensors, such that the drone would flip in the corresponding direction. The drone executed the orders accurately. The source file of the LabVIEW program used to control the remote-control system was uploaded as a supplementary material." The LabVIEW program for the remote-control system is shown in Figure R9 and has been added to the revised Supplementary Materials as Figure S27. The corresponding sentence on page 21 has been rewritten as: "A customized LabVIEW controlling program collects and processes the signal and sends it to a printed circuit board (PCB), which further generates a command to the drone via electromagnetic waves ( Figure   S27)." In addition, the source file of the LabVIEW program has been uploaded as a supplementary material. Response: We thank the reviewer for the kind suggestions. Indeed, adhesion between the printed sensor and other materials is an important issue in practical applications, but developing a new type of adhesive material and investigating its performances such as skin-friendliness, breathability, and flexibility deserve an independent project and are beyond the scope of current work. However, just as the robust adhesion achieved between the PEE and tango in the printed sensors, achieving robust adhesion between the sensor and other materials is feasible using the DLP-based 3D printing. For example, we use a layer of polyacrylamide hydrogel to adhere a printed sensor to various materials, including fabric, skin, plastic, and metal (aluminum alloy), as shown in Figure R10a. We further perform a 180° peeling test to probe the adhesion between a printed sensor and a non-woven fabric ( Figure R10b). Cohesive failure occurs along the hydrogel layer ( Figure R10c), which is an indicator of strong adhesion.
We add Figure R10 as Figure S28 in the revised Supplementary Materials and add the following sentences on page 22 in the revised manuscript: "Furthermore, the adhesion between the remote-control unit and the substrate is important in practical deployments.
Similar to the robust adhesion achieved between the PEE and DE in the printed sensors, achieving robust adhesion between the sensor and other materials is feasible using the DLP-based 3D printing. As an example, we print a layer of polyacrylamide hydrogel to strongly adhere a printed sensor to various materials, including fabric, skin, plastic, and metal ( Figure S28)." We also add a section, entitled "Adhering a printed sensor and a fabric using a hydrogel adhesive" in the Methods section: "A hydrogel solution was prepared using acrylamide (AAM) as the monomer, 0.625 mol% PEGDA as the crosslinker and 5 wt% TPO as the photo-initiator. AAM, PEGDA, and TPO were dissolved in deionized water with a water content of 80 wt% to form a transparent precursor as the printing ink. We first printed the sensor with a dimension of 50 mm × 10 mm × 1.5 mm (0.5 mm for each layer) following the same steps as before. Then we printed a layer of the hydrogel with a thickness of 1 mm as an adhesive layer on the sensor. After printing, the printed structure was subjected to UV light irradiation with 365 nm wavelength for 1 h for complete curing of the printed structure. For the 180° peeling test, a fabric was bonded onto the hydrogel and the test was performed on an Instron 5966 with a 100N load cell at a speed of 10 mm min −1 ." In addition, we would like to point out that the adhesion of soft materials and soft devices is an emerging subject of multi-disciplines such as mechanics, chemistry, topology, etc. Hydrogels as soft and wet adhesives for interfacing human beings and soft machines, as well as other types of soft adhesive materials, have been extensively investigated in recent years. Here we have only demonstrated the feasibility of achieving robust adhesion for the printed sensors using one type of hydrogel. A more comprehensive and systematic study of new adhesive materials requires additional investigations but is outside the scope of this work.

Comment 3.4:
Similarly, the deformation of ion-conductive materials can result in changes in electrical resistance. The question arises whether these resistance variations can impact the capacitance response characteristics of the devices. In general, when the resistance value undergoes variations, the capacitance value can also change accordingly.

Response:
We appreciate the reviewer for this insight. Both resistance and capacitance will change when the device is deformed. For ideal dielectrics and ideal conductors, both changes are the consequences of geometrical changes. Therefore, we agree with the reviewer that the resistance change of the polyelectrolyte elastomer will affect the response characteristics of the sensor, e.g. the charging and discharging time of the capacitor, the response speed, or the RC delay of the circuit. However, the resistance change barely alters the capacitance value of our sensor. As shown in Figure R11, we perform the following FEA for verification. We model the capacitive responses of a 10 mm wide parallel-plate capacitor, consisting of a layer of 2 mm thick dielectric elastomer sandwiched between two layers of 1 mm thick conductive elastomer, as shown in Figure R11a. The dielectric elastomer has a conductivity of = 10 −12 / and a relative dielectric constant of = 3 . The conductor elastomer has a conductivity of = 10 −2 / and a relative dielectric constant of = 30. The top electrode is applied with 1 V and the bottom electrode is grounded. We model each layer as a resistor in parallel with a capacitor ( Figure R11b).
The subscribe "1" represents the conductive elastomer and the subscribe "2" represents the dielectric elastomer. Without losing generality, we select three resistance values for R1, conduct FEA, and calculate the capacitance. As shown in Figure R11c, the capacitances are almost the same whereas the resistance value expands over three orders of magnitude.
The details about the modeling are as follows. Introduce the current conservation equation and make the material comply with Ohm's constitutive law. The conductivity and relative dielectric constant were set as 10 -12 S/m and 3 for dielectric elastomer, and 10 -2 S/m and 30 for conductive elastomer. Calculate the impedance using the current obtained from FEA, and then use the imaginary part of the impedance to calculate the capacitance value based on the selected circuit model.

Comment 3.5:
When collecting data from sensors with multiple channels, the possibility of signal crosstalk does exist. Signal crosstalk refers to the interference or coupling of signals between different channels, which can affect the accuracy of data acquisition. How to avoid signal crosstalk in separation of multiple strain information?

Response:
We appreciate the reviewer for this insight. Indeed, signal crosstalk is an important issue that needs to be carefully resolved when designing sensors with multimode sensing capabilities. The signals of different channels might interfere with each other. Since the capacitance change mainly depends on the geometrical change, the key to avoiding signal crosstalk is to minimize the associated deformation of other sensors when deforming one sensor through appropriate structure design. Thanks to the high flexibility in the structure design of multi-material 3D printing, we can design and fabricate integrated ionotronic sensors that can sense different stimuli without prominent mutual signal interferences.
As shown in Figure 5, we have fabricated three types of integrated ionotronic sensors.
Take the integrated tensile and compressive sensor as an example, we rationally design and fabricate an integrated tensile and compressive sensor that can decipher the signals of compression, tension, or their combination. The design and principle of the sensor are sketched in Figure R12a. The right part constitutes a compressive sensor monitored by the capacitance meter C1, and the bottom part constitutes a tensile sensor monitored by the capacitance meter C2. Note that the two sensors have one shared electrode and two independent electrodes, which are separated much farther than the thickness of the DE layer to minimize signal cross-talks. In addition, the DE layer of the tensile sensor is much thinner than the DE layer of the compressive sensor such that, the relative thickness change of the DE layer of the compressive sensor is negligible when the tensile sensor is activated. Specifically, the projected distance between the two independent electrodes is 13 mm, the thickness of the DE layer of the compressive unit is 2 mm, and the DE layer of the tensile unit is 1 mm, as shown in Figure R12b.
In addition, we have performed FEA and the results validate our design principles well.
As shown in Figure R12d & e, for a normal compressive strain of 25%, the principal strain of the compressive unit is ~0.328 while the principal strain of the tensile unit is only ~0.00873. Figure R12f shows the variation of ΔC/C0 with compressive strain for capacitance meters C1 and C2. The capacitance of C1 increases while the capacitance of C2 barely changes with compressive strain. As shown in Figure R12g & h, for a normal tensile strain of 100%, the principal strain of the tensile unit is ~0.483 while the principal strain of the compressive unit is only ~0.0029. Figure R12i shows the variation of ΔC/C0 with tensile strain for capacitance meters C1 and C2. The capacitance of C2 decreases while the capacitance of C1 barely changes with tensile strain.